add minimal README

README.md

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+---
+base_model: roberta-base
+language: en
+tags:
+  - narrative
+  - text-classification
+  - pytorch
+---
+
+# narrative-event-relation-roberta
+
+RoBERTa-base fine-tuned for event relation identification: binary temporal (sequential vs non-sequential) and causal classification. Uses entity markers [E1]/[E2] inserted at character offsets. Trained on LLM pseudo-labels with held-out human gold evaluation.
+
+> **Note:** Full model card with training details coming soon.
+
+## Loading
+
+Download `model.pt` and `tokenizer/` from this repo, then:
+
+```python
+import torch
+from transformers import AutoModel, AutoTokenizer
+from torch import nn
+
+ENTITY_MARKERS = ["[E1]", "[/E1]", "[E2]", "[/E2]"]
+
+class EventRelationRoBERTa(nn.Module):
+    def __init__(self, model_name):
+        super().__init__()
+        self.backbone = AutoModel.from_pretrained(model_name)
+        hidden = self.backbone.config.hidden_size
+        self.temporal_head = nn.Linear(hidden, 1)
+        self.causal_head   = nn.Linear(hidden, 1)
+
+    def forward(self, input_ids, attention_mask):
+        cls = self.backbone(input_ids=input_ids, attention_mask=attention_mask).last_hidden_state[:, 0, :]
+        return self.temporal_head(cls), self.causal_head(cls)
+
+tokenizer = AutoTokenizer.from_pretrained("tokenizer/")
+model = EventRelationRoBERTa("roberta-base")
+model.load_state_dict(torch.load("model.pt", map_location="cpu", weights_only=True))
+model.eval()
+```
+
+## Config
+
+```json
+{
+  "model_name": "roberta-base",
+  "max_len": 256,
+  "dims": [
+    "temporal_sequential",
+    "causal"
+  ],
+  "data_source": "/projects/tejo9855/Projects/llm-narrative-annotations/event_relation/outputs/google_gemma-4-31B-it/20260518_143249",
+  "n_train": 6219,
+  "n_val": 690,
+  "val_frac": 0.1,
+  "best_epoch": 4,
+  "seed": 42,
+  "test_f1_gold": 0.805
+}
+```